Mobile communication devices are increasingly being integrated with additional sensors. These sensors provide a variety of functionality such that mobile communication devices are becoming more powerful in determining a user's context and providing meaningful actions based on the determined context.
One such context determination is in-vehicle usage. A mobile communication device can determine whether a user is in a moving vehicle or not by using sensor data from one or more of an accelerometer and audio sensor and location data. After the mobile communication device makes a determination that the user is in a vehicle, it can adjust settings for hands-free mode and to facilitate the user focusing on the road while driving.
For example, some existing mobile communication devices can announce a caller's name and read out a text message for the user if the determined context is that the user is in a vehicle. This context detection response is desirable if the user is driving a car, because it facilitates the driver keeping her eyes on the road rather than being tempted to look at the mobile communication device. However, if the user is on public transportation such as a bus or train, the same response would be awkward because the user may not want her caller's name or text message to be read out in front of other people around her. Unfortunately, current in-vehicle context detection methods fail to distinguish between when the user is driving a car and riding on public transportation. This is because the data used for this purpose appears similar with respect to a private car and public transportation vehicles.
One known system for in-vehicle context detection in a mobile communication device is Google's activity recognition system (available at: http://developer.android.com/training/location/activity-recognition.html). This system which is based on the Android™ operating system can recognize various user activities and includes determining when the mobile communication device is in-vehicle. However, the system cannot distinguish whether a user is in a personal car or is riding on public transportation.
Another known system for in-vehicle context detection in a mobile communication device is described by Zheng et al in “Understanding Mobility Based on GPS Data,” ACM International Joint Conference on Pervasive and Ubiquitous Computing (2008) [hereinafter “Zheng”]. Zheng describes an approach for distinguishing among four classes that include walking, driving, bicycling and riding a bus, and uses GPS logs to make an inference of the class. However, Zheng does not provide any way to discriminate between a car and a bus, which is a form of public transportation, because the mobility patterns exhibited by GPS data are similar for a car and public transportation.
Another known system for in-vehicle context detection in a mobile communication device is described by Stenneth et al in “Transportation Mode Detection using Mobile Phones and GIS Information,” Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2011) [hereinafter “Stenneth”]. Stenneth describes an approach to detect different transportation modes including car, train, bus etc. by using accelerometer data in addition to GPS data and also using specific transportation network information. Stenneth requires the transportation network information, noting that the GPS and accelerometer readings are similar for cars, trains and buses.